Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.

Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.

The present disclosure describes an imaging system, method, and apparatus for identifying a latent image of a hidden object. A light source generates a first beam of narrow-band light and a second beam of narrow-band light that has temporal fluctuations correlated with the first beam. A frequency modulator shifts a temporal frequency of at least one of the first beam or the second beam. The first beam is directed towards a first scattering surface and the second beam is directed towards a second scattering surface. The first scattering surface scatters the first beam to a scattered light that illuminates a hidden object. The hidden object reflects at least a portion of the scattered light towards the second scattering surface, the reflected light interferes with the second beam and produces an interference pattern on the second scattering surface. A lock-in camera detects an irradiance of the interference pattern, monitors temporal variations of the irradiance caused by the temporal frequency shift introduced by the frequency modulator, and identifies a complex-valued light field that represents information of the hidden object based on the temporal variations of the irradiance.

A living body determination device includes: a light irradiation device that irradiates a measuring object with a first light including a plurality of spectrums; a spectroscopic device that disperses a light at intensity depending on a wavelength and outputs the light; an image acquisition device that receives the light output by the spectroscopic device and outputs image information representing brightness depending on the intensity of the light; and a control unit. The control unit, for each spectrum of the first light, acquires image information with respect to the measuring object from the image acquisition device, based on the image information, selects one or more areas, for each of the areas, acquires spectroscopic information, and based on whether the spectroscopic information satisfies a predetermined condition, determines whether the measuring object is a living body.

A method to incorporate multiple independent optical correlators into one system. By independent optical correlator, we mean an optical correlator comprising of an input SLM, filter SLM, and camera, combined with appropriate coherent illumination and Fourier transforming lenses. By one system we mean a single optical system which utilises the elements of each of the independent correlators multiple times.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

A method of image evaluation when performing SIM microscopy on a sample includes: A) providing n raw images of the sample, which were each generated by illuminating the sample with an individually positioned SIM illumination pattern and imaging the sample in accordance with a point spread function, B) providing (S1) n illumination pattern functions, which each describe one of the individually positioned SIM illumination patterns, C) providing (S1) the point spread function and D) Carrying out an iteration method, which includes following iteration steps a) to e), as follows: a) providing an estimated image of the sample, b) generating simulated raw images, in each case by image processing of the estimated image using the point spread function and one of the n illumination pattern functions such that n simulated raw images are obtained, c) assigning each of the n simulated raw images to that of the n provided raw images which was generated by the illumination pattern that corresponds to the illumination pattern function used to generate the simulated raw image, and calculating n correction raw images by the comparison of each provided raw image with the simulated raw image assigned thereto, d) generating a correction image by combining image processing of the n correction raw images using the point spread function and the n illumination pattern functions, wherein a filtering step is carried out in each implementation of iteration step d), said filtering step suppressing a spatial fundamental frequency of the illumination pattern, and e) reconstructing the estimated image of the sample by means of the correction image and using the corrected estimated image of the sample as the estimated image of the sample in iteration step a) in the next run through the iteration.

An electronic device is provided. The electronic device includes a transparent member comprising a transparent material, a display panel disposed under the transparent member and including a plurality of pixels, a biometric sensor disposed under the display panel, and a filter disposed between the display panel and the biometric sensor and covering the biometric sensor.

The inventive method involves receiving as input a representation of an ordered set of two-dimensional images. The ordered set of two-dimensional images is analyzed to determine at least one first view of an object in at least two dimensions and at least one motion vector. The next step is analyzing the combination of the first view of the object in at least two dimensions, the motion vector, and the ordered set of two-dimensional images to determine at least a second view of the object; generating a three-dimensional representation of the ordered set of two-dimensional images on the basis of at least the first view of the object and the second view of the object. Finally, the method involves providing indicia of the three-dimensional representation as an output.